Nonlinear Discriminant Analysis Research Articles

The use of artificial intelligence for predicting postinfarction myocardial viability in echocardiographic images.

Evaluation of standard echocardiographic examination with artificial intelligence may help in the diagnosis of myocardial viability and function recovery after acute coronary syndrome. Sixty-one consecutive patients with acute coronary syndrome were enrolled in the present study (43 men, mean age 61 ± 9 years). All patients underwent percutaneous coronary intervention (PCI). 533 segments of the heart echo images were used. After 12 ± 1 months of follow-up, patients had an echocardiographic evaluation. After PCI each patient underwent cardiac magnetic resonance (CMR) with late enhancement and low-dose dobutamine echocardiographic examination. For texture analysis, custom software was used (MaZda 5.20, Institute of Electronics).Linear and non-linear (neural network) discriminative analyses were performed to identify the optimal analytic method correlating with CMR regarding the necrosis extent and viability prediction after follow-up. Texture parameters were analyzed using machine learning techniques: Artificial Neural Networks, Namely Multilayer Perceptron, Nonlinear Discriminant Analysis, Support Vector Machine, and Adaboost algorithm. The mean concordance between the CMR definition of viability and three classification models in Artificial Neural Networks varied from 42% to 76%. Echo-based detection of non-viable tissue was more sensitive in the segments with the highest relative transmural scar thickness: 51-75% and 76-99%. The best results have been obtained for images with contrast for red and grey components (74% of proper classification). In dobutamine echocardiography, the results of appropriate prediction were 67% for monochromatic images. Detection and semi-quantification of scar transmurality are feasible in echocardiographic images analyzed with artificial intelligence. Selected analytic methods yielded similar accuracy, and contrast enhancement contributed to the prediction accuracy of myocardial viability after myocardial infarction in 12 months of follow-up.

Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis.

Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis. Thirty-three rabbits were randomly divided into 27 carbon tetrachloride-induced liver fibrosis group and 6 control group. Spectral CT contrast-enhanced scan was performed in batches, and the liver fibrosis was staged according to the histopathological results. The portal venous phase spectral CT parameters [70keV CT value, normalized iodine concentration (NIC), spectral HU curve slope (λHU)] were measured, and MaZda texture analysis was performed on 70keV monochrome images. Three dimensionality reduction methods and four statistical methods in B11 module were used to perform discriminant analysis and calculate misclassified rate (MCR), and ten texture features under the lowest combination of MCR were statistically analyzed. Receiver operating characteristic curve (ROC) was used to calculate the diagnostic performance of spectral parameters and texture features for significant liver fibrosis. Finally, the binary logistic regression was used to further screen independent predictors and establish model. A total of 23 experimental rabbits and 6 control rabbits were included, of which 16 had significant liver fibrosis. Three spectral CT parameters with significant liver fibrosis were significantly lower than those of non-significant liver fibrosis (p < 0.05), and the AUC ranged from 0.846 to 0.913. The combination analysis of mutual information (MI) and nonlinear discriminant analysis (NDA) had the lowest MCR, which with 0%. In the filtered texture features, four were statistically significant and AUC > 0.5, ranges from 0.764 to 0.875. The logistic regression model showed that Perc.90% and NIC could be used as independent predictors, the overall prediction accuracy of the model was 89.7% and the AUC was 0.976. Spectral CT parameters and texture features have high diagnostic value for predicting significant liver fibrosis in rabbits, and the combination of the two can improve its diagnostic efficiency.

Elevation determines the spatial risk of Anthrax outbreaks in Karnataka, India

Anthrax is an economically important livestock disease affecting subsistence farmers and it is of zoonotic importance. Anthrax is endemic in many states of India including Karnataka. Identification of spatial risk factors for occurrence of anthrax and development of predictive risk maps are required for planning adequate vaccination in high-risk areas as well as targeted surveillance activities in animals, humans and environment. In this study, village level anthrax outbreak locations from Karnataka (1997-2016) were geo-referenced and predictive risk map was developed using temporally Fourier Processed remotely sensed variables. A non-linear discriminant analysis approach was used to develop the risk map for Karnataka. Elevation was identified as top predictor variable in the 10 variables selected. The predicted risk map showed good accuracy and validation statistics when evaluated using different metrics (Kappa, sensitivity, specificity, AUC). The predicted risk map also showed good correspondence with past outbreaks. Further, we used Bayesian Penalised spline method to estimate species response curves for top 10 variables selected. The validated risk map can be used in planning vaccination strategy and surveillance in high-risk areas.

Computed tomography-based texture assessment for the differentiation of benign, borderline, and early-stage malignant ovarian neoplasms

This study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms. This retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist's misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to determine the performances of different parameters. Nonlinear discriminant analysis had a lower misclassification rate than the other analyses. Thirty texture parameters significantly differed between the two groups. In the training set, WavEnLH_s-3 and WavEnHL_s-3 were the optimal texture features during the regular phase, while WavEnHH_s-4 and Kurtosis seemed to be the most discriminative features during the portal venous phase. In the validation test, benign versus malignant tumors and benign versus borderline lesions were well-distinguished. Computed tomography-based texture features provide a useful imaging signature that may assist in differentiating benign, borderline, and early-stage ovarian cancer.

Nonlinear Discriminant Analysis Method Based on the Class Cover and Its Application

Nonlinear Discriminant Analysis Method Based on the Class Cover and Its Application

Proteomic analysis of urinary extracellular vesicles of kidney transplant recipients with BKV viruria and viremia: A pilot study.

To better define the biological machinery associated with BK virus (BKV) infection, in kidney transplantation, we performed a proteomics analysis of urinary extracellular vesicles (EVs). Twenty-nine adult kidney transplant recipients (KTRs) with normal allograft function affected by BKV infection (15 with only viremia, 14 with viruria and viremia) and 15 controls (CTR, KTRs without BKV infection) were enrolled and randomly divided in a training cohort (12 BKV and 6 CTR) used for the mass spectrometry analysis of the EVs (microvesicles and exosomes) protein content and a testing cohort (17 BKV and 9 CTR) used for the biological validation of the proteomic results by ELISA. Bioinformatics and functional analysis revealed that several biological processes were enriched in BKV (including immunity, complement activation, renal fibrosis) and were able to discriminate BKV vs. CTR. Kinase was the only gene ontology annotation term including proteins less abundant in BKV (with SLK being the most significantly down-regulated protein). Non-linear support vector machine (SVM) learning and partial least squares discriminant analysis (PLS-DA) identified 36 proteins (including DNASE2, F12, AGT, CTSH, C4A, C7, FABP4, and BPNT1) able to discriminate the two study groups. The proteomic profile of KTRs with BKV viruria alone vs. viremia and viruria was quite similar. Enzyme-linked immunosorbent assay (ELISA) for SLK, BPNT1 and DNASE2, performed on testing cohort, validated proteomics results. Our pilot study demonstrated, for the first time, that BKV infection, also in the viruric state, can have a negative impact on the allograft and it suggested that, whether possible, an early preventive therapeutic strategy should be undertaken also in KTRs with viruria only. Our results, then, revealed new mechanistic insights into BKV infection and they selected potential biomarkers that should be tested in future studies with larger patients' cohorts.

Machine vision-based Statistical texture analysis techniques for characterization of liver tissues using CT images.

To characterize human liver tissues by demonstrating the ability of machine vision, and to propose a new auto-generated report based on texture analysis that may work with co-occurrence matrix statistics. The retrospective study was conducted at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan, and comprised clinically verified computed tomography imaging data between October 2018 and September 2020. The image samples and related data were used to segregate classes 1-4. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues using supervised learning methods, principal component analysis, linear discriminant analysis, and non-linear discriminant analysis. Robust and reliable texture features were investigated by generating testing classes. Overall performance of the presented machine vision approach was analyzed using four parameters; precision, recall/sensitivity, F1-score, and accuracy. Statistical analysis was done using B11 software. There were 312 image samples from 71 patients; 51(71.8%) males and 20(28.2%) females. Among the patients, 19(26.7%) had abscess, 15(21.1%) had metastatic disease, 23(32.4%) had tumour necrosis, 6(8.5%) had vascular disorder, and 8(11.3%) were normal. Principal component analysis, linear discriminant analysis, and non-linear discriminant analysis showed high >97.86% values, but the discrimination rate was 100% for class 4. Abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques using second-order statistics that may assist the radiologist and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.

Usefulness of Fractal Analysis of Kirsch Edge Images for the Tissue Fragment Inner Structure in Breast FNAB

Objective: Recent advances in high-precision mammography and ultrasound screening have led to an increase in the detection of early lesions (ductal carcinoma in situ and small cancers) appearing as microcalcified lesions or microcystic images, and there needs to be an improvement in the accuracy of breast fine-needle aspiration biopsy (FNAB) assessing these lesions. The objective of this study was to investigate whether fractal analysis of Kirsch edge images for the tissue fragment inner structure (FKT) is useful in breast FNAB. FKT measures tissue fragment chromasia of hyperchromatic crowded tissue fragments (HCG), tissue fragment shape unevenness, and tissue fragment inner structure complexity. Study Design Materials: Nineteen epithelial tissue fragments of fibroadenoma (FA) from 7 patients and 52 tissue fragments of invasive breast carcinoma of no special type (IBC-NST) (grade 1–2) from 11 patients were assessed. First, tissue fragments were classified into small (smaller than 60 × 102 μm²), medium, and large (100 × 102 μm² or larger), and the appearance rate of each size was determined. Second, for FKT, the luminance value of tissue fragment chromasia, the unevenness and fractal value, and the tissue fragment inner structure complexity were determined. In statistical analysis, the Steel-Dwass test, nonlinear discriminant analysis, and receiver operating characteristic analysis were performed, setting the significance level at p < 0.05. Results: “Unevenness of the tissue fragment shape,” “fractal value of the tissue fragment shape,” and “fractal value of the tissue fragment inner structure” were significantly higher in small and large tissue fragments in IBC-NST compared with those in FA. The specificity and sensitivity were the highest (100%) in small tissue fragments in multivariate analysis using 4 variables (“luminance value of tissue fragment chromasia,” “unevenness of tissue fragment shape,” “fractal value of the tissue fragment shape,” and “fractal value of the tissue fragment inner structure”). Conclusion: FKT, which evaluates “tissue fragment darkness,” “tissue fragment shape unevenness,” and “tissue fragment inner structure complexity” focusing on small tissue fragments of HCG in breast FNAB, is useful as a system that assists cytopathological assessment of breast FNAB.

Improved window adaptive gray level co-occurrence matrix for extraction and analysis of texture characteristics of pulmonary nodules

Background and ObjectiveIdentifying benign and malignant pulmonary nodules is essential for the early diagnosis of lung cancer and targeted surgical resection. This study aimed to differentiate benign from malignant pulmonary nodules based on computed tomography (CT) plain scan texture analysis technique. MethodsA total of 47 pulmonary nodules use the improved window adaptive gray level co-occurrence matrix (GLCM) algorithm to extract the texture characteristics of the area of interest. The Fisher coefficient (Fisher), classification error probability joint average correlation coefficient (POE+ACC), mutual information (MI), and the combination of above three methods joint (FPM) were used to select the best texture parameters set. After that, the analysis of the screened texture parameters was adopted. The B11 module provides four analytical methods, including raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis (LDA), and nonlinear discriminant analysis (NDA). The results were expressed in the form of misclassification rate (MCR). Region of curve (ROC) analysis was also performed on the selected optimal texture parameters. ResultsThe MCR of all the three texture feature extraction methods, Fisher, POE+ACC, and MI, were lower in differentiating benign from malignant pulmonary nodules. FPM method could further reduce the MCR. The NDA analysis had the lowest MCR for both of these three feature extraction methods. The MCR can be further reduced to 2.13% by the combination of NDA and FPM. The ROC curve showed that Perc.01% parameter had the highest AUC value and the most discriminative efficacy. ConclusionThe lowest MCR values were calculated by the FPM dimensionality reduction method and the NDA analysis method. The improved GLCM algorithm has a discriminative role in CT texture analysis of benign and malignant pulmonary nodules.

Predicting response to non-small cell lung cancer immunotherapy using pre-treatment contrast-enhanced CT texture-based classification

Objective: To explore the value of pre-treatment contrast-enhanced computed tomography (CT)-based texture analysis in predicting response to non-small cell lung cancer (NSCLC) immunotherapy. Methods: From January to July 2018, a total of 51 lesions from 42 patients with advanced non-small cell lung cancer receiving immunotherapy at Shanghai Chest Hospital were selected in this retrospective study. Pre-treatment contrast-enhanced CT-based texture features were extracted by MaZda software. Ten optimal texture features were chosen based on three different methods: Fisher coefficient, mutual information measure (MI) and minimization of classification error probability combined average correlation coefficients(POE+ ACC), respectively. According to the efficacy of the first immunotherapy, 51 lesions were divided into non-progressive disease (non-PD, n=26) and progressive disease (PD, n=25). The differences were tested in each texture feature set between the two groups. The immunotherapy effects of target lesions were analyzed by principal component analysis(PCA), linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA). The sensitivity, specificity, accuracy, positive-predictive value (PPV) and negative-predictive value (NPV) were calculated. The area under the curve (AUC) was used to quantify the predictive accuracy of the three analysis models for each texture feature set and compare them with the actual classification results. Results: In all of three texture feature sets, the texture parameter differences of Perc.50%, Perc.90%, "S(5, 5)SumEntrp" and "S(4, 4)SumEntrp" were higher in PD group than those in non-PD group (all P<0.05). The classification result of texture feature set chosen by POE+ ACC and analyzed by NDA was identified as the best model (AUC=0.802, 95%CI: 0.674-0.930), and its sensitivity, specificity, accuracy, PPV and NPV were 72%, 88.5%, 80.4%, 85.7%, 76.7%, respectively. Conclusion: Pre-treatment contrast-enhanced CT-based texture characteristics of NSCLC may function as non-invasive biomarkers for the evaluation of response to immunotherapy.

Predicting SME’s default: Are their websites informative?

We propose the use of online indicators, scraped from the firms’ websites, to predict default risk for a sample of Spanish firms via nonlinear discriminant analysis and the logistic regression model.

Pre-Immunotherapy Contrast-Enhanced CT Texture-Based Classification: A Useful Approach to Non-Small Cell Lung Cancer Immunotherapy Efficacy Prediction.

Objective: To investigate the utility of the pre-immunotherapy contrast-enhanced CT-based texture classification in predicting response to non-small cell lung cancer (NSCLC) immunotherapy treatment.Methods: Sixty-three patients with 72 lesions who received immunotherapy were enrolled in this study. We extracted textures including histogram, absolute gradient, run-length matrix, gray-level co-occurrence matrix, autoregressive model, and wavelet transform from pre-immunotherapy contrast-enhanced CT by using Mazda software. Three different methods, namely, Fisher coefficient, mutual information measure (MI), and minimization of classification error probability combined average correlation coefficients (POE + ACC), were performed to select 10 optimal texture feature sets, respectively. The patients were divided into non-progressive disease (non-PD) and progressive disease (PD) groups. t-test or Mann–Whitney U-test was performed to test the differences in each texture feature set between the above two groups. Each texture feature set was analyzed by principal component analysis (PCA), linear discriminant analysis (LDA), and non-linear discriminant analysis (NDA). The area under the curve (AUC) was used to quantify the predictive accuracy of the above three analysis models for each texture feature set, and the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were also calculated, respectively.Results: Among the three texture feature sets, the texture parameter differences of kurtosis (2.12 ± 3.92 vs. 0.78 ± 1.10, p = 0.047), “S(2,2)SumEntrp” (1.14 ± 0.31 vs. 1.24 ± 0.12, p = 0.036), and “S(1,0)SumEntrp” (1.18 ± 0.27 vs. 1.28 ± 0.11, p = 0.046) between the non-PD and PD group were statistically significant (all p < 0.05). The classification result of texture feature set selected by POE + ACC and analyzed by NDA was identified as the best model (AUC = 0.812, 95% CI: 0.706–0.919) with a sensitivity, specificity, accuracy, PPV, and NPV of 88.2, 76.3, 81.9, 76.9, and 87.9%, respectively.Conclusion: Pre-immunotherapy contrast-enhanced CT-based texture provides a new method for clinical evaluation of the NSCLC immunotherapy efficacy prediction.

Comparison of various data analysis techniques applied for the classification of oligopeptides and amino acids by voltammetric electronic tongue

Abstract This work reports a critical evaluation of performance of various data analysis techniques applied to the classification of biosamples using voltammetric electronic tongue. Data obtained by sensor array of carbon-paste electrodes were processed by 3 pattern recognition methods: Partial Least Squares Discriminant Analysis (PLS-DA) as the “golden standard” in electronic tongue data analysis, linear Soft Independent Modelling of Class Analogy (SIMCA) and non-linear Support Vector Machine Discriminant Analysis (SVM-DA), the two latter being less commonly used. Due to high dimensionality of the data, various preprocessing methods (autoscaling, Standard Normal Variable – SNV, Discrete Wavelet Transform – DWT) were tested for each kind of these techniques to find most suitable workflow allowing to obtain satisfactory performance. Totally 1026 models were developed and tested. In order to compare their ability to estimate class affinity of the studied biosamples, 4 performance parameters: accuracy, sensitivity, precision, and specificity, were calculated for every model for both train and test sets to obtain reliable and repeatable results. It must be underlined, that some general remarks and findings that had been concluded during classification of oligopeptides, were later additionally validated using different dataset (comprising oligopeptides and amino acids). The treatment used confirmed that the presented recommendations can be generalized and adapted to other classification tasks by means of voltammetric electronic tongues.

Predicting the prognosis of hepatocellular carcinoma with the treatment of transcatheter arterial chemoembolization combined with microwave ablation using pretreatment MR imaging texture features

ObjectiveTo investigate the prognostic value of baseline magnetic resonance imaging (MRI) texture analysis of hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolization (TACE) and microwave ablation (MWA).MethodsMRI was performed on 102 patients with HCC before receiving TACE combined with MWA in this retrospective study. The best 10 texture features were screened as a feature group for each MRI sequence by MaZda software using mutual information coefficient (MI), nonlinear discriminant analysis (NDA) and other methods. The optimal feature group with the lowest misdiagnosis rate was achieved on one MRI sequence between two groups dichotomized by 3-year survival, which was used to optimize the significant texture features with the optimal cutoff values. The Cox proportional hazards model was generated for the significant texture features and clinical variables to determine the independent predictors of overall survival (OS). The predictive performance of the model was further evaluated by the area under the ROC curve (AUC). Kaplan–Meier and log-rank tests were performed for disease-free survival (DFS) and Local recurrence-free survival (LRFS).ResultsThe optimal feature group with the lowest misdiagnosis rate of 8.82% was obtained on T2WI using MI combined with NDA feature analysis. For Cox proportional hazards regression models, the independent prognostic factors associated with OS were albumin (P = 0.047), BCLC stage (P = 0.001), Correlat(1,− 1)T2 (P = 0.01) and SumEntrp(3,0)T2 (P = 0.015), and the prediction efficiency of multivariate model is AUC = 0.876, 95%CI = 0.803–0.949. Kaplan–Meier analyses further demonstrated that BCLC (P < 0.001), Correlat(1,− 1)T2 (P = 0.023) and SumEntrp(3,0)T2 (P < 0.001) were associated with DFS, and BCLC (P = 0.007) related to LRFS.ConclusionsMR imaging texture features may be used to predict the prognosis of HCC treated with TACE combined with MWA.

Estimation of Sexual Maturity Based on Morphometrics of Genital Organs in Male Northern Fur Seals, Callorhinus ursinus

It's not always possible to obtain samples from dead male pinnipeds that are fresh enough to determine the histological characteristics of spermatogenesis and their stage of sexual maturity. We propose that the morphometrics of genital organs of northern fur seals, Callorhinus ursinus, can be used as a new indicator of sexual maturity. We analyzed data from collected records of male fur seals around Hokkaido during 2011–2017 to examine morphometrics of genital organs and their stage in histological characteristics of spermatogenesis. First, we investigated ontogenetic and seasonal variation in three genital organs: testes, epididymides, and bacula. Second, we classified the stages of histological characteristics of spermatogenesis into three groups (Stage A–C) using non-linear discriminant analysis, based on the stage in histological characteristics of spermatogenesis determined by histological observation of seminiferous tubules. Testicular size, testicular mass, and epididymal mass showed seasonal variation associated with spermatogenesis. From our analysis, the stage in histological characteristics of spermatogenesis could be estimated using testicular mass with high accuracy of predicting spermatogenesis. And measurement of baculum was suggested to be an indicator of predicting sexual maturity. We conclude that this simple estimation may be used to determine sexual maturity based on individual growth patterns.

Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study

BackgroundTo explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC).MethodOne hundred four participants were enrolled in this retrospective study. Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture features were analyzed by MaZda, and B11 program was used for data analysis and classification. The diagnosis efficiencies of texture features and conventional imaging features in identifying the differentiated degree of HCC were assessed by receiver operating characteristic analysis. The relationship between texture features and differentiated degree of HCC was evaluated by Spearman’s correlation coefficient.ResultsThe grey-level co-occurrence matrix -based texture features were most frequently extracted and the nonlinear discriminant analysis was excellent with the misclassification rate ranging from 3.33 to 14.93%. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively, while the AUC of tumor size was 0.649, 0.660 and 0.517 respectively. The tumor size was significantly different between poorly- and moderately-HCC (p = 0.014). The COMBINE AUC values were not increased with tumor size combined.ConclusionsTexture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI was valuable and might be a promising method in identifying the differentiated degree of HCC. The poorly-differentiated HCC was more heterogeneous than well- and moderately-differentiated HCC.

Nested AdaBoost procedure for classification and multi-class nonlinear discriminant analysis

AdaBoost methods find an accurate classifier by combining moderate learners that can be computed using traditional techniques based, for instance, on separating hyperplanes. Recently, we proposed a strategy to compute each moderate learner using a linear ensemble of weak classifiers that are built through the kernel support vector machine (KSVM) hypersurface geometry. In this way, we apply AdaBoost procedure in a nested loop: Each iteration of the inner loop boosts weak classifiers to a moderate one while the outer loop combines the moderate classifiers to build the global decision rule. In this paper, we explore this methodology in two ways: (a) For classification in principal component analysis (PCA) spaces; (b) For multi-class nonlinear discriminant PCA, named MNDPCA. Up to the best of our knowledge, the former is a new AdaBoost-based classification technique. Besides, in this paper we study the influence of kernel types for MNDPCA in order to set a near optimum configuration for feature selection and ranking in PCA subspaces. We compare the proposed methodologies with counterpart ones using facial expressions of the Radboud Faces database and Karolinska Directed Emotional Faces (KDEF) image database. Our experimental results have shown that MNDPCA outperforms counterpart techniques for selecting PCA features in the Radboud database while it performs close to the best technique for KDEF images. Moreover, the proposed classifier achieves outstanding recognition rates if compared with the literature techniques.

The feasibility of differentiating colorectal cancer from normal and inflammatory thickening colon wall using CT texture analysis

To investigate the diagnostic value of texture analysis (TA) for differentiating between colorectal cancer (CRC), colonic lesions caused by inflammatory bowel disease (IBD), and normal thickened colon wall (NTC) on computed tomography (CT) and assess which scanning phase has the highest differential diagnostic value. In all, 107 patients with CRC, 113 IBD patients with colonic lesions, and 96 participants with NTC were retrospectively enrolled. All subjects underwent multiphase CT examination, including pre-contrast phase (PCP), arterial phase (AP), and portal venous phase (PVP) scans. Based on these images, classification by TA and visual classification by radiologists were performed to discriminate among the three tissue types. The performance of TA and visual classification was compared. Precise TA classification results (error, 2.03–12.48%) were acquired by nonlinear discriminant analysis for CRC, IBD and NTC, regardless of phase or feature selection. PVP images showed a better ability to discriminate the three tissues by comprising the three scanning phases. TA showed significantly better performance in discriminating CRC, IBD and NTC than visual classification for residents, but there was no significant difference in classification between TA and experienced radiologists. TA could provide useful quantitative information for the differentiation of CRC, IBD and NTC on CT, particularly in PVP images.

Real-time Burn Classification using Ultrasound Imaging

This article presents a real-time approach for classification of burn depth based on B-mode ultrasound imaging. A grey-level co-occurrence matrix (GLCM) computed from the ultrasound images of the tissue is employed to construct the textural feature set and the classification is performed using nonlinear support vector machine and kernel Fisher discriminant analysis. A leave-one-out cross-validation is used for the independent assessment of the classifiers. The model is tested for pair-wise binary classification of four burn conditions in ex vivo porcine skin tissue: (i) 200 °F for 10 s, (ii) 200 °F for 30 s, (iii) 450 °F for 10 s, and (iv) 450 °F for 30 s. The average classification accuracy for pairwise separation is 99% with just over 30 samples in each burn group and the average multiclass classification accuracy is 93%. The results highlight that the ultrasound imaging-based burn classification approach in conjunction with the GLCM texture features provide an accurate assessment of altered tissue characteristics with relatively moderate sample sizes, which is often the case with experimental and clinical datasets. The proposed method is shown to have the potential to assist with the real-time clinical assessment of burn degrees, particularly for discriminating between superficial and deep second degree burns, which is challenging in clinical practice.

The value of CT radiomics in the prediction of EGFR mutation in lung cancer

Objective: To explore the value of CT radiomics quantitative features in the prediction of epidermal growth factor receptor (EGFR) mutation in lung cancer. Methods: The data of 144 patients, 75 males, 69 females, median age 54 (25-68 years), with EGFR gene test results in lung cancers diagnosed in the First Affiliated Hospital of Soochow University were retrospectively analyzed, including 81 patients, 39 males, 42 females, median age 52 (25-64)years old, with EGFR mutations and 63 patients,36 males,27 females,median age 56(32-68) years old,with EGFR wild types. According to a ratio of 2︰1, patients were randomly assigned to the training group and validation group. MaZda software was used to extract radiomics features including the gray level histogram (GLH), absolute gradient (GRA), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), auto-regressive model (ARM) and wavelets transform (WAV), and so on. Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE+ACC) and mutual information (MI) were used to select 10 optimal features making up the optimal feature subsets. The optimal feature subsets were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) to calculate the accuracy, sensitivity and specificity in the differential diagnosis of EGFR mutant types and wild types in lung cancers. The prediction model was established using the optimal feature subsets with the highest accuracy in the training group with artificial neural network (ANN). The established prediction model was used to differentiate EGFR mutant types from wild types in the validation group. Results: MaZda software extracted a total of 301 quantitative features in the CT images for the patients with EGFR mutant types and EGFR wild types in the training group. The optimal feature subsets obtained from Fisher-NDA and (POE+ACC)-NDA had the highest accuracy of 93.8%, in the differential diagnosis of the EGFR mutant types and EGFR wild types of lung cancer in the training group. The optimal feature subset prediction model obtained from Fisher-NDA had the accuracy, sensitivity and specificity of 83.3%, 86.7% and 77.8%, respectively, in the differential diagnosis of the EGFR mutant types and EGFR wild types of lung cancer in the validation group. Conclusion: The optimal subset of CT radiomics features has high accuracy in predicting EGFR mutations in lung cancer, providing a new method for predicting gene expression of lung cancer.